Diagnosis of central nervous system white matter pathology using diffusion mri

ABSTRACT

A method of performing diffusion basis spectrum imaging (DBSI) within a tissue of a patient using diffusion magnetic resonance data acquired from a portion of the tissue is disclosed. The diffusion magnetic resonance data includes a plurality of diffusion MR signals associated with one voxel and the one voxel represents an image of the portion of the tissue. The method includes computing an isotropic diffusion portion of the diffusion magnetic resonance data representing isotropic diffusion within the one voxel and dividing the isotropic diffusion portion, which includes a fraction of the diffusion magnetic resonance data representing isotropic diffusion, into a restricted isotropic diffusion portion and a non-restricted isotopic diffusion portion. The restricted isotropic diffusion portion includes a fraction of the isotropic diffusion portion with an apparent diffusion coefficient of less than 0.3 and the non-restricted isotopic diffusion portion includes a fraction of the isotropic diffusion portion with an apparent diffusion coefficient of at least 0.3.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.13/109,986 entitled DIAGNOSIS OF CENTRAL NERVOUS SYSTEM WHITE MATTERPATHOLOGY USING DIFFUSION MRI filed on May 17, 2011, the entirety ofwhich is hereby incorporated by reference. U.S. patent application Ser.No. 13/109,986 claims the benefit of U.S. Provisional Patent ApplicationSer. No. 61/345,367 entitled DIAGNOSIS OF CENTRAL NERVOUS SYSTEM WHITEMATTER PATHOLOGY USING DIFFUSION MRI filed on May 17, 2010, the entiretyof which is hereby incorporated by reference.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH & DEVELOPMENT

This invention was made with government support under grantsR01-NS047592, R01-NS054194, and P01-NS059560, awarded by the U.S.National Institutes of Health. The government has certain rights in theinvention.

BACKGROUND

Aspects of the disclosure relate generally to magnetic resonance imaging(MRI) and, more particularly, to diffusion magnetic resonance dataprovided by an MRI scanner.

White matter injury is common in central nervous system (CNS) disordersand plays an important role in neurological dysfunctions in patients.Understanding the pathology of complex and heterogeneous central nervoussystem diseases such as multiple sclerosis (MS) has been greatlyhampered by the dearth of histological specimens obtained seriallyduring the disease. Clinicians are reluctant to perform invasive CNSbiopsies on patients with white matter disorders, due to the potentialinjury to the patients.

The insight of CNS white matter neuropathology has been derivedtypically from occasional biopsies consisting of small tissue samples ofunusual cases. These autopsies usually derive from patients withend-stage disease and often have long postmortem delay artifacts duetissue degradation. It is therefore advantageous to have a noninvasiveimaging tool to accurately quantify and better understand the chronicand non-fatal injury in CNS disease during the whole course of theindividual patient.

Diffusion tensor imaging (DTI) is a commonly used MRI modality in CNSdisease/injury diagnosis. However, the current use of DTI technique isnot capable of resolving the complex underlying pathologies correctly,despite being considered better than other techniques.

BRIEF DESCRIPTION

In one aspect, a method is provided for performing diffusion basisspectrum imaging (DBSI) within a tissue of a patient using diffusionmagnetic resonance data acquired from a portion of the tissue. Thediffusion magnetic resonance data includes a plurality of diffusion MRsignals associated with one voxel, and the one voxel represents an imageof the portion of the tissue. The method includes computing, by aprocessor, an isotropic diffusion portion of the diffusion magneticresonance data representing isotropic diffusion within the one voxel.The isotropic diffusion portion includes a fraction of the diffusionmagnetic resonance data representing isotropic diffusion. The methodfurther includes dividing, by the processor, the isotropic diffusionportion into a restricted isotropic diffusion portion and anon-restricted isotopic diffusion portion. The restricted isotropicdiffusion portion includes a fraction of the isotropic diffusion portionwith an apparent diffusion coefficient of less than 0.3, and thenon-restricted isotopic diffusion portion includes a fraction of theisotropic diffusion portion with an apparent diffusion coefficient of atleast 0.3.

In another aspect, a method is provided for diagnosing an inflammatorydisorder within a tissue of a patient using diffusion basis spectrumimaging (DBSI) data derived from diffusion magnetic resonance dataacquired from a portion of the tissue. The diffusion magnetic resonancedata includes a plurality of diffusion MR signals associated with onevoxel, and the one voxel represents an image of the portion of thetissue. The method includes computing, by a processor, an isotropicdiffusion portion of the diffusion magnetic resonance data representingisotropic diffusion within the one voxel. The isotropic diffusionportion includes a fraction of the diffusion magnetic resonance datarepresenting isotropic diffusion. The method further includes dividing,by the processor, the isotropic diffusion portion into a restrictedisotropic diffusion portion and a non-restricted isotopic diffusionportion. The restricted isotropic diffusion portion includes a fractionof the isotropic diffusion portion with an apparent diffusioncoefficient of less than 0.3, and the non-restricted isotopic diffusionportion includes a fraction of the isotropic diffusion portion with anapparent diffusion coefficient of at least 0.3. The method furtherincludes comparing, by the processor, the restricted isotropic portionto an inflammation threshold and identifying, by the processor, aninflammatory condition within the portion of the tissue if therestricted isotropic portion exceeds the inflammation threshold.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an illustration of diffusion magnetic resonance (MR) signalresponse when diffusion tensor imaging (DTI) is applied to a singlewhite matter tract of coherent axonal fibers.

FIG. 2A is an illustration of an exemplary DTI result corresponding to ascenario in which an ideal coherent single fiber (spinal cord whitematter or optic nerve) is included within a scanned volume. FIG. 2B isan illustration of an exemplary DTI result corresponding to a scenarioin which a fiber and an isotropic component (tissue loss, inflammation,or edema) are included within a scanned volume. FIG. 2C is anillustration of an exemplary DTI result corresponding to a scenario inwhich two crossing fibers are included within a scanned volume. FIG. 2Dis an illustration of an exemplary DTI result corresponding to ascenario in which two crossing fibers with an isotropic components areincluded within a scanned volume.

FIG. 3A is a flowchart of an exemplary noninvasive process to quantifycomplex CNS white matter pathology.

FIGS. 3B and 3C are a flowchart of an exemplary method for determiningdiffusivities of fibers and isotropic components within a tissue.

FIG. 4A is a 2D schematic illustration of the design of an exemplary99-direction diffusion-weighting scheme. FIG. 4B is a 3D schematicillustration of the design of the exemplary 99-directiondiffusion-weighting scheme shown in FIG. 4A.

FIG. 5 is an illustration of an exemplary diffusion basis set for DBSI.

FIG. 6A is an illustration of a first iteration of an exemplaryoptimization process of a DBSI basis set. FIG. 6B is an illustration ofa second iteration of an exemplary optimization process of a DBSI basisset. FIG. 6C is an illustration of a third iteration of an exemplaryoptimization process of a DBSI basis set. FIG. 6D is an illustration ofa fourth iteration of an exemplary optimization process of a DBSI basisset. FIG. 6E is an illustration of a DBSI basis set resulting from anexemplary optimization process illustrated in FIG. 6A, FIG. 6B, FIG. 6C,and FIG. 6D.

FIG. 7A is an illustration of determining the number of fibers andprimary directions of candidate fibers using DBSI for a single-fibertract. FIG. 7B is an illustration of determining the number of fibersand primary directions of candidate fibers using DBSI using a two-fibertract.

FIG. 8A is an illustration of a first optimization of an exemplaryoptimization process for determining the directional diffusivity of eachcandidate fiber, isotropic components and corresponding volume ratiosusing DBSI. FIG. 8B is an illustration of a second optimization of theexemplary optimization process illustrated in FIG. 8A. FIG. 8C is anillustration of a third optimization of the exemplary optimizationprocess illustrated in FIG. 8A and FIG. 8B. FIG. 8D is an illustrationof a fourth optimization of the exemplary optimization processillustrated in FIG. 8A, FIG. 8B, and FIG. 8C.

FIG. 9A and FIG. 9B are illustrations of diffusion basis spectrumimaging (DBSI) images of control (FIG. 9A) and cuprizone-fed (FIG. 9B)male C57BL/6 mice, reflecting demyelination as increased radialdiffusivity in the presence of axonal injury, and inflammation incontrast to the failure of DTI to detect the pathology.

FIG. 10 is a graph of myelin basic protein in a MBP-positive areaderived from DBSI of FIG. 9A and FIG. 9B.

FIG. 11 is a graph of the radial diffusivity derived using DBSI of FIG.9A and FIG. 9B.

FIG. 12A and FIG. 12B are illustrations of DBSI results for 4-weektreated (FIG. 12A) and control (FIG. 12B) mice, detecting axonal injuryin the presence of demyelination and inflammation.

FIG. 13 is a graph of the axial diffusivity of the DBSI of FIG. 12A andFIG. 12B.

FIG. 14 is a graph of the SMI-31 stain of the DBSI of FIG. 12A and FIG.12B.

FIG. 15 is a graph of axonal fiber tract density was also derived usingDBSI expressing as volume ratio of the DBSI of FIG. 12A and FIG. 12B.

FIG. 16A and FIG. 16B are illustrations of DBSI results for 4-weektreated (FIG. 16A) and control (FIG. 16B) mice, quantifying inflammationin the presence of axonal injury and demyelination.

FIG. 17 is a graph of the percentage of inflammatory cell infiltrationthought to be in the cells of the illustration of FIG. 16A and FIG. 16B.

FIG. 18A is an exemplary phantom of mouse trigeminal nerves embedded ingel with known in vivo DTI characteristics. FIG. 18B is an imageillustrating the DTI result for the exemplary phantom shown in FIG. 18A.FIG. 18C is a table summarizing the DTI-obtained characteristics of theexemplary phantom illustrated in FIG. 18A. FIG. 18D is an imageillustrating the DBSI result for isotropic diffusion for the exemplaryphantom shown in FIG. 18A. FIG. 18E is an image illustrating the DBSIresult for anisotropic diffusion for the exemplary phantom shown in FIG.18A. FIG. 18F is a table summarizing the DBSI-obtained characteristicsof the exemplary phantom illustrated in FIG. 18A. FIG. 18G is an imageof a second fiber phantom.

FIG. 19A, FIG. 19B, FIG. 19C, FIG. 19D, FIG. 19E, and FIG. 19F includeimages comparing the results of diffusion spectrum imaging (DSI) andDBSI from a human subject. FIG. 19A is a schematic illustration of adiffusion spectrum imaging (DSI) method. FIG. 19B is an imageillustrating crossed fibers identified using DSI. FIG. 19C is a DTIimage of crossed fibers. FIG. 19D is an isotropic diffusion component ofcrossed fibers obtained using a DBSI method. FIG. 19E is an anisotropicdiffusion component of crossed fibers obtained using a DBSI method. FIG.19F is an illustration of DSI results obtained from crossed fibers usinga DSI method.

FIG. 20A is a diffusion tensor imaging (DTI) for mouse trigeminal nerveembedded in gel. FIG. 20B is a table summarizing the DTI-obtainedcharacteristics of the mouse trigeminal nerve embedded in gel.

FIG. 21A is an image of a phantom that includes a mouse trigeminal nerveembedded in gel. FIG. 21B is a DBSI result for anisotropic diffusion formouse trigeminal nerve embedded in gel. FIG. 21C is a DBSI result forisotropic diffusion for mouse trigeminal nerve embedded in gel. FIG. 21Dis a table summarizing the DTI-obtained characteristics of the mousetrigeminal nerve embedded in gel.

FIG. 22A is schematic illustration of heterogeneous pathology 2200within one image voxel of interested white matter lesion. FIG. 22B is aschematic illustration of a normal axon component within the one imagevoxel illustrated in FIG. 22A. FIG. 22C is a schematic illustration ofan axon component with myelin damage within the one image voxelillustrated in FIG. 22A. FIG. 22D is a schematic illustration of aninjured axon component within the one image voxel illustrated in FIG.22A. FIG. 22E is a schematic illustration of an injured axon with myelindamage within the one image voxel illustrated in FIG. 22A. FIG. 22F is aschematic illustration of an inflammatory cell component within the oneimage voxel illustrated in FIG. 22A. FIG. 22G is a schematicillustration of a tissue loss component within the one image voxelillustrated in FIG. 22A.

FIG. 23A is schematic illustration of summing anisotropic tensorsrepresentative of normal axon density and demyelinated axon density togenerate an approximation of immunohistochemical SMI-31+ staining forintact axons. FIG. 23B is a schematic illustration of summinganisotropic tensors representative of injured axon density andinjured/demyelinated axon density to generate an approximation ofimmunohistochemical SMI-32+ staining for injured axons. FIG. 23C is aschematic illustration of summing anisotropic tensors representative ofnormal axon density and injured axon density to generate anapproximation of immunohistochemical MBP+staining for axons with intactmyelin. FIG. 23D is a schematic illustration of summing isotropicdiffusion components to generate an approximation of immunohistochemicalDAPI+ staining for cell nucleus.

FIG. 24A is a detailed view of the DBSI-derived MBP fraction of the scanof FIG. 23C.

FIG. 24B is a detailed view of the DBSI-derived SMI-31 fraction of thescan of FIG. 23A.

FIG. 24C is a detailed view of the DBSI-derived DAPI fraction of thescan of FIG. 23D.

FIG. 24D is a detailed view of the DBSI-derived water fraction of thescan of FIG. 23B.

FIG. 25A is a detailed view of the DBSI-derived MBP fraction of the scanof FIG. 24A. FIG. 25B, FIG. 25C, FIG. 25D, FIG. 25E, and FIG. 25F areconventional invasive histology images from five selected regions 2502,2504, 2506, 2508, and 2510, respectively, as marked in FIG. 25A.

FIG. 26A is a detailed view of the DBSI-derived SMI-31 intensity of thescan of FIG. 24B. FIG. 26B, FIG. 26C, FIG. 26D, FIG. 26E, and FIG. 26Fare conventional invasive histology images from five selected regions2602, 2604, 2606, 2608, and 2610, respectively, as marked in FIG. 26A.

FIG. 27A is a detailed view of the DBSI-derived DAPI intensity of thescan of FIG. 24C. FIG. 27B, FIG. 27C, FIG. 27D, FIG. 27E, and FIG. 27Fare conventional invasive histology images from five selected regions2702, 2704, 2706, 2708, and 2710, respectively, as marked in FIG. 27A.

FIG. 28A and FIG. 28B are illustrations of DAPI (FIG. 28A) and SMI-31(FIG. 28B) staining of a fixed mouse trigeminal nerve and a comparisonof isotropic diffusion spectra with gel.

FIG. 29 is a graph of the nucleus and axon counts by IHC of FIG. 29.

FIG. 30 is a graph of the DBSI derived cell percentage and fiberpercentage of FIG. 29.

FIG. 31 is an illustration of a typical DBSI-derived spectrum ofisotropic diffusivity from a fixed mouse trigeminal nerve juxtaposedwith gel.

FIG. 32 is a comparison of DBSI-derived gel fractions to those measuredby T2W MRI signal intensity.

FIG. 33 is a graph of λ_(∥) derived from trigeminal nerves with andwithout gel.

FIG. 34 is a graph of λ_(⊥) derived from trigeminal nerves with andwithout gel.

FIG. 35A is a DBSI of a pair of nerve fibers crossing at 92° andjuxtaposed with 2% agarose gel. FIG. 35B is a DBSI of a pair of nervefibers crossing at 55° and juxtaposed with 2% agarose gel. FIG. 35C is aDBSI of a pair of nerve fibers crossing at 28° and juxtaposed with 2%agarose gel. FIG. 35D is a DSI-derived ODF of a pair of nerve fiberscrossing at 92° and juxtaposed with 2% agarose gel. FIG. 35E is an ODFof a pair of nerve fibers crossing at 55° and juxtaposed with 2% agarosegel. FIG. 35F is an ODF of a pair of nerve fibers crossing at 28° andjuxtaposed with 2% agarose gel. FIG. 35G is a general q-sampling image(GQI) of a pair of nerve fibers crossing at 92° and juxtaposed with 2%agarose gel. FIG. 35H is a GQI of a pair of nerve fibers crossing at 55°and juxtaposed with 2% agarose gel. FIG. 35I is a GQI of a pair of nervefibers crossing at 28° and juxtaposed with 2% agarose gel.

FIG. 36A is an image of a three-fiber crossing phantom forming atriangle. FIG. 36B is a DBSI of the three-fiber crossing phantom shownin FIG. 36A.

FIG. 37A is a graph of an axial diffusivity λ_(1∥) of a first fiber.

FIG. 37B is a graph of a volume ratio fλ_(∥) of a second fiber.

FIG. 37C is a graph of axial diffusivity λ_(2∥) of the second fiber.

FIG. 37D is a graph of radial diffusivity λ_(2⊥) of the second fiber.

FIG. 38A is an MC-simulation-derived graph displaying fiber ratio, waterratio, cell ratio, cell ADC, and fiber radial diffusivity of diffusionMRI data generated in silico.

FIG. 38B is an MC-simulation-derived graph displaying fiber axialdiffusivity, water ADC of diffusion MRI data generated in silico.

FIG. 39 is a CRLB based optimization of a one-fiber and a two-isotropiccompartments diffusion model.

FIG. 40A, FIG. 40B, FIG. 40C, FIG. 40D, FIG. 40E, and FIG. 40F areschematic illustrations of a DBSI-derived signature of homogeneouspathologies. FIG. 40A is a schematic illustration of a normal axoncomponent. FIG. 40B is a schematic illustration of an axon componentwith myelin damage. FIG. 40C is a schematic illustration of an injuredaxon component. FIG. 40D is a schematic illustration of an injured anddemyelinated axon. FIG. 40E is a schematic illustration of an isotropicdiffusion component corresponding to invasive cells 2210. FIG. 40F is aschematic illustration of a tissue loss component.

FIG. 41A is an illustration of the procedure to calculate individualpathology maps. FIG. 41B is a schematic illustration of an individualpathology map.

FIG. 42A is an illustration of in vivo DBSI derived λ_(∥) of a B6-EAEmouse at baseline. FIG. 42B is an illustration of in vivo DBSI derivedλ_(∥) of a B6-EAE mouse at EAE onset. FIG. 42C is an illustration of invivo DBSI derived λ_(∥) of a B6-EAE mouse at EAE peak. FIG. 42D is anillustration of in vivo DBSI derived λ_(∥) of a B6-EAE mouse at chronicEAE. FIG. 42E is an illustration of in vivo DBSI derived λ_(⊥) of aB6-EAE mouse at baseline. FIG. 42F is an illustration of in vivo DBSIderived λ_(⊥) of a B6-EAE mouse at EAE onset. FIG. 42G is anillustration of in vivo DBSI derived λ_(⊥) of a B6-EAE mouse at EAEpeak. FIG. 42H is an illustration of in vivo DBSI derived λ_(⊥) of aB6-EAE mouse at chronic EAE. FIG. 42I is an illustration of in vivo DBSIderived cell fraction of a B6-EAE mouse at baseline. FIG. 42J is anillustration of in vivo DBSI derived cell fraction of a B6-EAE mouse atEAE onset. FIG. 42K is an illustration of in vivo DBSI derived cellfraction of a B6-EAE mouse at EAE peak. FIG. 42L is an illustration ofin vivo DBSI derived cell fraction of a B6-EAE mouse at chronic EAE.FIG. 42M is a corresponding SMI-31-stained image of a B6-EAE mouse atbaseline. FIG. 42N is a corresponding SMI-31-stained image of a B6-EAEmouse at EAE onset. FIG. 42O is a corresponding SMI-31-stained image ofa B6-EAE mouse at EAE peak. FIG. 42P is a corresponding SMI-31-stainedimage of a B6-EAE mouse at chronic EAE. FIG. 42Q is a correspondingMBP-stained image of a B6-EAE mouse at baseline. FIG. 42R is acorresponding MBP-stained image of a B6-EAE mouse at EAE onset. FIG. 42Sis a corresponding MBP-stained image of a B6-EAE mouse at EAE peak. FIG.42T is a corresponding MBP-stained image of a B6-EAE mouse at chronicEAE. FIG. 42U is a corresponding DAPI-stained image of a B6-EAE mouse atbaseline. FIG. 42V is a corresponding DAPI-stained image of a B6-EAEmouse at EAE onset. FIG. 42W is a corresponding DAPI-stained image of aB6-EAE mouse at EAE peak. FIG. 42X is a corresponding DAPI-stained imageof a B6-EAE mouse at chronic EAE.

FIG. 43 is a cross-sectional time course of in vivo DBSI derived λ_(∥)from B6-EAE mice at baseline (control), onset, peak, and chronic diseasestates.

FIG. 44 is a cross-sectional time course of in vivo DBSI derived λ^(⊥)from B6-EAE mice at baseline (control), onset, peak, and chronic diseasestates.

FIG. 45 is a cross-sectional time course of in vivo DBSI derived cellintensity percentage from B6-EAE mice at baseline (control), onset,peak, and chronic disease states.

FIG. 46 is a cross-sectional time course of in vivo DBSI derived waterintensity percentage from B6-EAE mice at baseline (control), onset,peak, and chronic disease states.

FIG. 47A is an ex vivo DBSI-derived T2W map of a human MS autopsy spinalcord specimen. FIG. 47B is an ex vivo DBSI-derived λ_(∥) map of a humanMS autopsy spinal cord specimen. FIG. 47C is an ex vivo DBSI-derived T2Wmap of a human MS autopsy spinal cord specimen. FIG. 47D is an ex vivoDBSI-derived λ_(⊥) map of a human MS autopsy spinal cord specimen.

FIG. 48A is an ex vivo DAPI-stained histology image of a human MSautopsy spinal cord specimen corresponding to region 4708 of FIG. 47B.FIG. 48B is an ex vivo DAPI-stained histology image of a human MSautopsy spinal cord specimen corresponding to region 4710 of FIG. 47B.FIG. 48C is an ex vivo DAPI-stained histology image of a human MSautopsy spinal cord specimen corresponding to region 4712 of FIG. 47B.FIG. 48D is an ex vivo SMI-31-stained histology image of a human MSautopsy spinal cord specimen corresponding to region 4714 of FIG. 47C.FIG. 48E is an ex vivo SMI-31-stained histology image of a human MSautopsy spinal cord specimen corresponding to region 4716 of FIG. 47C.FIG. 48F is an ex vivo SMI-31-stained histology image of a human MSautopsy spinal cord specimen corresponding to region 4718 of FIG. 47C.FIG. 48G is an ex vivo SMI-31-stained histology image of a human MSautopsy spinal cord specimen corresponding to region 4720 of FIG. 47D.FIG. 48H is an ex vivo SMI-31-stained histology image of a human MSautopsy spinal cord specimen corresponding to region 4722 of FIG. 47D.FIG. 481 is an ex vivo SMI-31-stained histology image of a human MSautopsy spinal cord specimen corresponding to region 4724 of FIG. 47D.

FIG. 49A is a first T1W MRI of a B6-EAE mouse spinal cord at baseline.FIG. 49B is a first T1W MRI of a B6-EAE mouse spinal cord at EAE onset.FIG. 49C is a first T1W MRI of a B6-EAE mouse spinal cord at EAE peak.FIG. 49D is a first T1W MRI of a B6-EAE mouse spinal cord at chronicEAE. FIG. 49E is a second T1W MRI of a B6-EAE mouse spinal cord atbaseline. FIG. 49F is a second T1W MRI of a B6-EAE mouse spinal cord atEAE onset. FIG. 49G is a second T1W MRI of a B6-EAE mouse spinal cord atEAE peak. FIG. 49H is a second T1W MRI of a B6-EAE mouse spinal cord atchronic EAE. FIG. 49I is a third T1W MRI of a B6-EAE mouse spinal cordat baseline. FIG. 49J is a third T1W MRI of a B6-EAE mouse spinal cordat EAE onset. FIG. 49K is a third T1W MRI of a B6-EAE mouse spinal cordat EAE peak. FIG. 49L is a third T1W MRI of a B6-EAE mouse spinal cordat chronic EAE.

FIG. 50 is a quantitative analysis of percentage enhancement map ofFIGS. 49A, 49B, 49C, 49D, 49E, 49F, 49G, 49H, 49I, 49J, 49K, and 49L.

DETAILED DESCRIPTION

Embodiments provided herein employ a diffusion MRI technique tononinvasively study and quantify complicated CNS diseases in anoninvasive fashion without the limitation of invasive histologicalexaminations.

Such embodiments facilitate improved results compared to diffusiontensor imaging (DTI). The directional diffusivities derived from DTImeasurements describe water movement parallel to (λ_(∥), axialdiffusivity) and perpendicular to (λ_, radial diffusivity) axonaltracts. We have previously proposed and validated that decreased λ_(∥)is associated with axonal injury and dysfunction, and increased λ_(⊥) isassociated with myelin injury in mouse models of white matter injury.

The presence of inflammation, edema, or gliosis during CNS white matterinjury may impact the DTI measurement. One significant effect ofinflammation is the resulting isotropic component of diffusion, due tothe increased extracellular water and the infiltrating immune cells.This component complicates the DTI measurements and distorts theestimated directional diffusivity and anisotropy preventing its accurateinterpretation of underlying pathologies. In addition to inflammation,similar isotropic diffusion tensor component may result from the loss ofCNS tissues in the chronic MS lesions, spinal cord injury (SCI), ortraumatic brain injury (TBI). The currently used DTI protocol is notable to resolve this isotropic component or differentiate inflammationfrom tissue loss. Only an averaged diffusion tensor reflecting theoverall effect can be obtained from existing DTI methods.

DTI fails to (1) correctly describe axonal fiber directions in crossingwhite matter tracts, or (2) accurately reflect the complex white matterpathologies such as vasogenic edema, inflammation, and tissue losscommonly coexisting with axonal and myelin damages. Even recentlydeveloped existing systems are not capable of resolving white matterpathologies in complex tissue scenarios.

A noninvasive process based on diffusion MRI technique is describedherein to facilitate accurately quantifying the complex human CNS whitematter pathology where the current DTI and its relevant improvementshave failed. As an exemplary embodiment, diffusion basis spectrumimaging (DBSI) is implemented and provided herein to demonstrate thefeasibility and detailed operation of the proposed novel process. Thequantity and primary direction of diffusion tensor components within atissue volume resulting from white matter pathology is determined usingdiffusion MRI before constructing the multi-tensor model. After theidentification of each diffusion tensor component corresponding toindividual pathology, the diffusivity and volume ratio of each componentcan be derived accordingly.

In some embodiments, the quantity of candidate fibers and theirassociated primary directions are calculated first by DBSI based on acombination of diffusion basis set best describing the measureddiffusion magnetic resonance data. An isotropic diffusion component isalso considered to improve the computation accuracy. Based on allcandidate fibers' primary directions, DBSI is used to compute the axialdiffusivity, indicating water diffusion parallel to the fiber, andradial diffusivity, indicating water diffusion perpendicular to thefiber. A diffusivity spectrum of isotropic diffusion components, such asthose resulting from inflammation or tissue loss, as well as associatedvolume ratios of all candidate fibers and isotropic components may becalculated.

An exemplary embodiment employs diffusion basis spectrum imaging (DBSI)to facilitate an accurate diagnosis of CNS white matter pathology. Eachdiffusion tensor's directional diffusivity as well as its primaryorientation is derived using the less stringent diffusion tensoracquisition schemes retaining DTI's applicability in clinical settings.Preliminary data in mouse corpus callosum, spinal cord injury, andphantoms demonstrates that DBSI is capable of identifying differentunderlying pathologies accurately estimating the extent of cellinfiltration, axonal fiber density in corpus callosum of cuprizonetreatment, as well as estimating tissue loss in chronic mouse spinalcord injury. Novel diffusion phantoms have also been designed andfabricated for a quantitative evaluation of DBSI and existing DTImethods.

The exemplary embodiment of diffusion MRI described herein resolves themulti-tensor complication resulting from diverse pathologies in CNSwhite matter to quantitatively derive diffusion parameters of crossingfibers as well as reflecting the actual pathologies. This uniquecapability of the proposed process and the exemplary DBSI method has thepotential to differentiate acute inflammation from chronic tissue lossin patients. Such capability can estimate the extent of acuteinflammation guiding the use of anti-inflammatory treatment and chronictissue damage guiding the effort in axonal/neuronal preservation. Thereare many potential clinical applications of the proposed process. Forexample, it can document the efficacy of stem cell treatment in axonalregeneration by clearly estimating the isotropic component of theimplanted cells while reflecting the axonal regeneration by quantifyingthe anisotropic component changes after cell transplantation. It couldalso be used to estimate the degree of CNS tumor growth by accuratelyestimating the isotropic tensor component representing the tumor cells.Methods described further facilitate evaluating the effectiveness of adrug in treating one or more medical conditions. For example, DBSI couldbe applied in clinical drug trial treating CNS diseases, tumors, andinjury by accurately reflecting the progression of clinical andpreclinical pathologies.

One important characteristic of DTI is its ability to measure diffusionanisotropy of CNS tissues for a detailed description of the underlyingtissue injury based on the changed diffusion character. However, suchmeasurement is not always obtainable in diseased tissues due to thecomplicated cellular responses to the pathology or the presence ofcrossing fibers.

The fundamental operation of DTI 10 can be explained by examining an MRIsignal 12 under the influence of diffusion weighting gradients 14. Whenapplying DTI to measure the single white matter tract of coherent axonalfibers, the MRI signal response can be expressed as shown in FIG. 1.

DTI assumes that there is only a pure coherent axonal fiber tract in themeasured tissue and the signal response to diffusion weighting gradientsis well described by the diffusion weighted (DW) profile. Theinsufficiency of DTI can be demonstrated by examining the diffusionellipsoid responding to the different tissue components that typicallyseen in CNS tissues with and without pathology, as shown in FIG. 2A,FIG. 2B, FIG. 2C, and FIG. 2D.

FIG. 2A, FIG. 2B, FIG. 2C, and FIG. 2D illustrate exemplary DTI results26, 28, 30, and 32, respectively, corresponding to scenarios with thedifferent tissue components (objects), including FIG. 2A illustratingideal coherent single fiber 20 (spinal cord white matter or opticnerves), FIG. 2B illustrating fiber 20 plus an isotropic component 22(tissue loss, inflammation, or edema), FIG. 2C illustrating two crossingfibers 20 and 24, and FIG. 2D illustrating two crossing fibers 20 and 24with an isotropic component 22. If fiber 20 of FIG. 2A is of interestand the target for a DTI measurement as demonstrated, the correct DTIresult for the ideal fiber result 26. Nevertheless, the various mixedconditions result in misrepresentations 28, 30, and 32 of the targetedfiber, which is the major shortcoming of DTI.

To definitively resolve the issue regarding the utility of directionaldiffusivity in detecting white matter injury in MS and/or other CNSwhite matter disorders, a careful evaluation was performed on the mousemodel of cuprizone intoxication that is widely employed to examine themechanisms of CNS white matter de- and re-myelination. It has beendemonstrated that axonal injury, inflammation, and demyelinationco-exist at 4 weeks of continuous cuprizone feeding. Our previous DTIstudies showed that decreased λ_(∥) correlated with histology-confirmedaxonal injury, while no significant increase of λ_(⊥) was seen, thusfailing to reflect the concurrent demyelination. A Monte Carlosimulation modeling the three underlying pathologies was performed.Preliminary results suggested that the presence of infiltratinginflammatory cells exerted significant effect on the derived directionaldiffusivity reducing both λ_(∥) and λ_(⊥), exaggerating the effect ofaxonal injury while diminishing the sensitivity to demyelination. Thisfinding suggests that the current DTI analysis is suboptimal toaccurately depict the underlying pathology in diseases withinflammation, such as MS.

To address this shortcoming of DTI, a novel process allowing an accuratedescription of the underlying tissue pathology is described herein. FIG.3A is a flow chart 100 illustrating the basic steps required to detectand differentiate the underlying CNS white matter pathologies. First, amulti-direction, multi-weighting diffusion MRI scan is conducted 102utilizing a signal acquisition and processing component. A multi-tensordiffusion model is constructed 104, and the multi-tensor model is solved106 to obtain the parameters and coefficients of the model.

In the exemplary embodiment, a multiple-tensor based DBSI, ordiffusivity component, is provided (FIGS. 3B and 3C). The methodillustrated may be used to determine diffusivity of each diffusiontensor component within a tissue. In the multiple-tensor based DBSI, anMRI scan is performed 108. In performing the MRI scan, subjects are setup 110 in MRI scanner and a multi-direction diffusion MRI scan isperformed 112. From the performed 112 MRI scan, a diffusion MRI datasetis obtained 114.

After an MRI scan is performed 108, number of fibers and their primaryorientation is determined 115. In determining 115 the number of fibersand their primary orientation a diffusion MRI signal is projected 116onto diffusion a basis and a computation error is evaluated. Next, anonlinear optimization procedure is performed 118 to compute optimizeddirectional diffusivities for diffusion basis. It is determined 120whether the fibers are converged and optimized. If the fibers aredetermined 120 not to have been converged and optimized, the currentdirectional diffusivities for both diffusion basis and isotropiccomponents are updated 122. After update 122, a diffusion basis usingcurrent directional diffusivities and isotropic component is constructed124 and projected 116 is performed again. If the fibers are determined120 to have been converged and optimized, the number of fibers based onprojection of diffusion MRI data onto optimized diffusion basis set isdetermined 126.

After the number of fibers and their primary orientation is determined115, diffusivities of each fiber and isotropic components are determined128. In determining 128 the diffusivities of each fiber and isotropiccomponents, a multi-tensor model with isotropic component using currentdirectional diffusivities for each fiber is constructed 130. Amulti-tensor model is solved 132 and evaluated for computational error.Next, a nonlinear optimization procedure is performed 134 to computeoptimized directional diffusivities for each fiber. It is determined 136whether the fibers are converged and optimized. If the fibers aredetermined 136 not to have been converged and optimized, the currentdirectional diffusivities for each fiber are updated 138 and themulti-tensor model is constructed 130 again. If the fibers aredetermined 136 to have been converged and optimized, a final directionaldiffusivity for each fiber is computed 140. Additionally, a meandiffusivity of each isotropic component, and a volume ratio of allcomponents is computed 140.

FIG. 4A and FIG. 4B are illustrations of the design of an exemplary99-direction diffusion-weighting scheme. As shown in the 2D schematic142 of FIG. 4A, each diffusion-weighting direction is selected based onthe grid point location. For example, as illustrated in FIG. 4B, thefirst diffusion weighting direction 144 is from origin (0, 0) to gridpoint (1, 0), the second diffusion weighting direction 146 is from (0,0) to (1, 1), and so on. In the exemplary embodiment, 99 diffusiondirections are selected based on the 3D grid locations 148 shown by 3Dmodel 150.

An advantage of designing the 99-direction diffusion weighting gradients148 based on regular grid locations is that the directions are uniformlysampled in the 3D space. No matter which direction the real axonal fiberorients, the scheme has no bias to it. Another advantage is that theweighting of diffusion gradients is naturally set as different values inthis grid-based design, which is favorable in terms of determiningmultiple isotropic diffusion components.

However, embodiments described herein are not limited to this particulardesign. Any diffusion-weighting scheme that samples the whole 3D spaceuniformly and provides multiple weighting factors will work wellresolving multiple-tensor reflecting the CNS white matter pathology asproposed.

Similar to diffusion basis function decomposition (DBFD) proposed byRamirez-Manzanares et al., DBSI employs the following multi-tensor modelas the first-step analysis:

$\begin{matrix}{S_{k} = {\sum\limits_{i = 1}^{N}{s_{i}\mspace{11mu} {\exp \left( {{- \overset{\rightarrow}{b_{k}}} \cdot \lambda_{\bot}} \right)} {\exp\left( {{{{- \overset{\rightarrow}{b_{k}}} \cdot \left( {\lambda_{\parallel} - \lambda_{\bot}} \right)}*\left. \quad{\cos^{2}\left( \theta_{i} \right)} \right)},{k = 1},2,{\ldots \mspace{14mu} 99}} \right.}}}} & \left( {{Equation}\mspace{14mu} 1} \right)\end{matrix}$

In Equation 1, {right arrow over (b)}_(k) is k^(th) diffusion gradient(k=1, 2, . . . , 99); λ_(∥) is the axial diffusivity and λ_(⊥) is theradial diffusivity; S_(k) is the measured diffusion weighted signal atdirection {right arrow over (b)}_(k); θ_(i) is the angle between thediffusion gradient {right arrow over (b)}_(k) and the primary directionof i^(th) diffusion basis; N is the number of diffusion basis componentsuniformly distributed in 3D space.

FIG. 5 illustrates a diffusion basis set 152 with 40 diffusion bases154. As shown in FIG. 5, each diffusion basis 154 represents a candidatefiber orientation, and the diffusion basis 154 set is uniformlydistributed in the 3D space. As described by Equation 1, the real fiberis treated as the linear combination of the entire diffusion basis set.

Instead of presetting λ_(∥) and λ_(⊥) at fixed values for the entirediffusion basis in DBFD, DBSI performs a nonlinear searching to estimatethe optimal values of λ_(∥) and λ_(⊥) best fitting the acquireddiffusion weighted data. Isotropic tensor component is uniquelyincorporated in DBSI to improve the accuracy, as shown in Equation 2:

$\begin{matrix}{{f\left( {\lambda_{\parallel},\lambda_{\bot},d} \right)} = {\min  {{\sum\limits_{k = 1}^{99}\left\{ {S_{k} - {\sum\limits_{i = 1}^{N}{S_{i}\mspace{11mu} {\exp \left( {{- {\overset{\rightarrow}{b}}_{k}} \cdot \lambda_{\bot}} \right)} {\exp\left( {{- {\overset{\rightarrow}{b}}_{k}} \cdot \left. \quad{{\left( {\lambda_{\parallel} - \lambda_{\bot}} \right)\left. \quad{\cos^{2}\left( \theta_{i} \right)} \right)} - {S_{N + 1}{\exp \left( {{\overset{\rightarrow}{b}}_{k} \cdot d} \right)}}} \right\}^{2}} \right.}}}} \right.}}}} & \left( {{Equation}\mspace{14mu} 2} \right)\end{matrix}$

In Equation 2, S_(i) (i=1, 2, . . . , N+1)≧0, λ_(∥) and λ_(⊥) aredirectional diffusivities, and d is the diffusivity of isotropicdiffusion component with d, λ_(∥), and λ_(⊥) selected as theoptimization variables. Unknown coefficients S_(i)(i=1, 2, . . . , N−1)are not optimization variables because are not independent to λ_(∥) or∥_(⊥). S_(i) are computed using the least square estimation under thenonnegative constraint (S_(i)≧0) and the basic principle of sparsity asemployed in DBFD during the nonlinear optimization procedure. After theoptimization, the number of fibers and their primary axis directions areestimated similar to DBFD.

A unique feature of this disclosure is that the shape of each diffusionbasis is not prefixed as in DBFD method. Instead, the basis shape isoptimized during the optimization process to estimate both λ_(∥) andλ_(⊥). This optimization process is demonstrated in FIG. 6A, FIG. 6B,FIG. 6C, FIG. 6D, and FIG. 6E, using a single axonal fiber 156 as theexample. In the exemplary embodiment, experimental data is fitted by thelinear combination of a diffusion basis set 154 with fitting errorimproved through iterations 158 (FIG. 6A), 160 (FIG. 6B), 162 (FIG. 6C),and 164 (FIG. 6D) until the optimal coefficients of linear combinationof diffusion basis are estimated 166 (FIG. 6E). In the exemplarembodiment, iteration 158 has a fitting error of 0.6, iteration 160 hasa fitting error of 0.4, iteration 162 has a fitting error of 0.2, anditeration 164 has a fitting error of 0.04. Isotropic component is alsoconsidered according to Equation 2 in this process (not shown) toimprove the optimization accuracy.

As shown in FIG. 7A and FIG. 7B, the diffusion basis 154 with directionclose to that of the axonal fiber 156 contributes more significantly tothe linear combination with higher magnitude of the coefficients S_(i).The diffusion basis 154 with direction away from that of the axonalfiber 156 has limited contribution to the coefficient of linearcombination of the basis set fitting the experimental data. Both single168 and two-fiber 170 tracts are demonstrated.

DBSI determines the number and primary direction of fibers according tothe description of Equation 1. Each coefficient is associated with onediffusion tensor basis at a particular direction. These preliminarycoefficients are grouped based on the magnitude and the closeness inorientations of the associated basis diffusion tensor. Coefficientssmaller than a threshold determined by raw signal SNR are ignored.Significant coefficients with closely oriented (within 15 degrees)diffusion basis tensors are grouped as one fiber. The threshold of 15degrees is set based on the desired angular resolution. Once thegrouping process is complete, the averaged direction of the groupeddiffusion basis is defined as the primary direction of the fiber.

Based on the number of fiber (anisotropic tensor) components andassociated primary directions, DBSI constructs another multi-tensormodel with the assumption of axial symmetry. A set of isotropic tensorcomponents are included in the model:

$\begin{matrix}{{h\left( {\lambda_{\parallel_{- i}},\lambda_{\bot_{- i}},{i = {1\mspace{14mu} \ldots \mspace{14mu} L}}} \right)} = {\min  {{\sum\limits_{k = 1}^{99}\left\{ {S_{k} - {\sum\limits_{i = 1}^{L}{S_{i}\mspace{11mu} {\exp \left( {{- {\overset{\rightarrow}{b}}_{k}} \cdot \lambda_{\bot{\_ i}}} \right)} {\exp\left( {{- {\overset{\rightarrow}{b}}_{k}} \cdot {\quad{\left( {\lambda_{\parallel {\_ i}} - \lambda_{\bot{\_ \; i}}} \right) \left. \quad{\left. \quad{\cos^{2}\left( \varnothing_{i} \right)} \right) - {\sum\limits_{j = 1}^{M}{S_{{L + j}\;}\mspace{11mu} {\exp \left( {{\overset{\rightarrow}{b}}_{k} \cdot d_{j}} \right)}}}} \right\}^{2}} }} \right.}}}} \right.}}}} & \left( {{Equation}\mspace{14mu} 3} \right)\end{matrix}$

In Equation 3, S_(k) is the measured diffusion weighted signal atdiffusion gradient direction {right arrow over (b)}_(k). L is the numberof estimated fibers in the imaging voxel. λ_(∥) _(_) _(i) and λ_(⊥) _(_)_(i) (i=1, 2, . . . , L) are the axial and radial diffusivity of the ithfiber. φ_(i) is the angle between the diffusion gradient {right arrowover (b)}_(k) and the primary direction of ith estimated fiber. d_(j)(j=1, . . . , M) are the diffusivities of M isotropic diffusioncomponents. S_(i) (i=1, 2, . . . , L) are fiber volume ratios and S_(i)(i=L+1, L+2, . . . , L+M) are the volume ratio of isotropic components.

Based on this multi-tensor model, a nonlinear optimization search isconstructed as following:

$\begin{matrix}{{h\left( {\lambda_{\parallel_{- i}},\lambda_{\bot_{- i}},{i = {1\mspace{14mu} \ldots \mspace{14mu} L}}} \right)} = {\min  {{\sum\limits_{k = 1}^{99}\left\{ {S_{k} - {\sum\limits_{i = 1}^{L}{S_{i}\mspace{11mu} {\exp \left( {{- {\overset{\rightarrow}{b}}_{k}} \cdot \lambda_{\bot{\_ \; i}}} \right)} {\exp\left( {{- {\overset{\rightarrow}{b}}_{k}} \cdot {\quad{\quad\left( {\lambda_{\parallel {\_ i}} - {\left. \quad\lambda_{\bot{\_ \; i}} \right) \left. \quad{\left. \quad{\cos^{2}\left( \varnothing_{i} \right)} \right) - {\sum\limits_{j = 1}^{M}{S_{{L + j}\;}\mspace{11mu} {\exp \left( {{\overset{\rightarrow}{b}}_{k} \cdot d_{j}} \right)}}}} \right\}^{2}}}  \right.}}} \right.}}}} \right.}}}} & \left( {{Equation}\mspace{14mu} 4} \right)\end{matrix}$

Equation 4 is subject to S_(i) (i=1, 2, . . . , L+M)≧0 In thisoptimization procedure, isotropic diffusivity d_(j) (j=1, . . . , M) arenot selected as optimization variables to reduce the total number of thefree variables. Instead, isotropic diffusivities are uniformly presetwithin the physiological range. Directional diffusivities, λ_(∥) _(_)_(i) and λ__(_) _(i) (i−1, . . . , L) of each anisotropic component arethe only free variables to be optimized based on the experimental dataand Equation 4 with the nonnegative constraint (S_(i)≧0). All diffusiontensor's volume ratios S_(i) (i=1, 2, . . . , L+M) based on T2-weighted(i.e., non-diffusion weighted) image intensity are computed with leastsquare fitting during the nonlinear optimization procedure.

In one embodiment, an optimization process 170, as shown in FIG. 8A,FIG. 8B, FIG. 8C, and FIG. 8D, is used to search the best directionaldiffusivities for each candidate fiber and compute all the volume ratiosof each diffusion component. Process 170 demonstrates two crossingfibers (L=2). In such an embodiment, a first optimization 174 (FIG. 8A)includes candidate fibers 175 with a fitting error of 0.4. Likewise, asecond optimization 176 (FIG. 8B) includes candidate fibers 175 with afitting error of 0.2, a third optimization 178 (FIG. 8C) includescandidate fibers 175 with a fitting error of 0.1, and a fourthoptimization 180 (FIG. 8D) includes candidate fibers 175 with a fittingerror of 0.02.

After the fourth optimization 180, the fitting error is smaller than 2%,which falls within the acceptable range. Therefore, the directionaldiffusivity of each candidate fiber 175, and corresponding volume ratioscomputed after the optimization 180 are determined as the final DBSIresults. In the DBSI algorithm, the nonlinear optimization procedure isexecuted based on criteria including maximal iteration numbers,tolerance of mesh size, tolerance of variable, tolerance of function,accepted accuracy, and many other criteria set according to the need.Once some or all of these criteria are met according to the presetlevel, the optimization procedure is considered satisfactorily fit thedata and the optimization stops.

To determine the capability of the newly developed DBSI approach indetecting and differentiating the underlying co-existing pathology, thecuprizone model was again employed to compare conventional DTI with thenew DBSI analysis. Striking contrast between DTI and DBSI was observedat the corpus callosum from C57BL/6 mice treated with cuprizone for 4weeks. DTI failed to detect demyelination and overestimated axonalinjury even with 99-direction diffusion weighting, while offering noinformation on inflammation. However, DBSI correctly reflected thepresence of demyelination (FIG. 9B), axonal injury (FIG. 10), andinflammation (FIG. 11).

FIG. 9A and FIG. 9B are illustrations of Sagittal view of corpuscallosum from a control 182 (FIG. 9A) and a 4-week cuprizone fed maleC57BL/6 mice (n=5) 184 (FIG. 9B) examined using DBSI and DTI. As shownby myelin basic protein 186 immunostaining, significant demyelination inthe caudal corpus callosum is seen by reduced MBP-positive area 188(FIG. 10) and increased radial diffusivity 190 (FIG. 11) derived usingDBSI. Consistent with previous reports, lack of increase in DTI derivedradial diffusivity failed to reflect the histological finding ofdemyelination (FIG. 12A).

FIG. 12A and FIG. 12B illustrate that similar to previous findings thatdecreased DTI derived axial diffusivity was seen in corpus callosum from4-week treated mice 184 (n=5, −43%) (FIG. 12A) from control 182 (FIG.12B), DBSI derived axial diffusivity 192 (FIG. 13) decreased (−31% fromthe control 182) to reflect the histology proved axonal injury (FIG.14). The axonal fiber tract density 194 (FIG. 15) was also derived usingDBSI expressing as volume ratio. Due to the infiltrating inflammatorycells, the density of axonal fiber tracts was reduced from 93% to 77%, afinding not available for conventional DTI.

FIG. 16A and FIG. 16B illustrate inflammatory cell infiltration 196derived using DBSI. In such an embodiment, the inflammatory cellinfiltration 196 is to be 16.9% (20.4-3.5) of total volume in 4-weekcuprizone treated corpus callosum 184 (FIG. 16A), above the baseline3.5% cellular content (FIG. 16B). This is consistent with thesignificantly increased DAPI positive stains in the same region,information has not been available using DTI.

In another embodiment, 99-direction diffusion weighted images areanalyzed following one or more operations described above to determinethe number of intravoxel fibers and isotropic components on a laboratoryfabricated phantom containing mouse trigeminal nerves with known in vivoDTI character and isotropic gel as shown in FIG. 18A.

Diffusion weighted MRI was performed on the phantom using 99 distinctdiffusion weighting gradients for both DTI 200 and DBSI 202 analysis.For the pure gel, DTI 200 and DBSI 202 estimated the isotropic apparentdiffusion coefficient to be identical at 1.91 μm²/ms suggesting bothmethods are accurate for simple medium. When examining the mixture offiber/gel in this phantom using DTI 202, the isotropic gel component wasnot identified (see FIG. 18B). In addition, the true fiber diffusionanisotropy (FA=0.82±0.005) determined previously using an in vivo highresolution DTI was not obtained (see FIG. 18C). In contrast, using thenewly proposed DBSI identified a fiber ratio 204 of 21%, a gel ratio 206of 74%, and a cell ratio 208 of 5% (see FIG. 18D and FIG. 18F) withcorrect fiber diffusion anisotropy of FA=0.83 (see FIG. 18E). Theanisotropy was compared because it was previously observed thatdiffusion anisotropy is preserved in vivo and ex vivo in mouse nervefibers.

Another fiber phantom 210 (FIG. 18G) was built to contain two mousetrigeminal nerves crossing each other at 90° with isotropic gel. Asexpected that DTI failed to identify the two crossing fibers or the gel.In contrast, DBSI was able to identify the presence of two fiberscrossing at 90° estimating fiber orientations of (1, 0, 0) and (0, 0,1). The diffusion anisotropy of the two fibers was estimated to be 0.81and 0.83 respectively. Correct volume ratio was also estimated by DBSIto report 19% of (1, 0, 0) fiber, 19% of (0, 0, 1) fiber, 52% of gel,and 10% of cell component.

In the chronic CNS injury, tissue loss is common. Current DTI techniqueshave not been able to correctly reflect the status of chronic tissueinjury. In a mouse spinal cord injury model, we examined the non-injuredand moderately injured cord tissues. In the non-injured white matter ofthe mouse spinal cord, the DTI derived diffusion parameters wereADC=0.29 μm²/ms, axial diffusivity=0.69 μm²/ms, radial diffusivity=0.12μm²/ms, and FA=0.85. These are comparable with those obtained using DBSIwhere ADC=0.29 μm²/ms, axial diffusivity=0.69 μm²/ms, radialdiffusivity=0.10 μm²/ms, and FA=0.85. Both DTI and DBSI were successfulin describing the non-injured white matter characteristics. However,when the moderately injured spinal cord tissues were examined, the DTIfailed to capture the underlying pathology, i.e., the extent of tissueloss, resulting in overestimating axial diffusivities thusunderestimating the severity of the injury. In contrast, DBSI was ableto estimate that there is a 10% tissue loss in the injured white matter.

Methods described herein facilitate determination of an axialdiffusivity, a radial diffusivity, and/or a volume ratio of a scannedvolume of tissue with increased accuracy relative to known methods,which are distinguishable at least as follows.

FIG. 19A, FIG. 19B, FIG. 19C, FIG. 19D, FIG. 19E, and FIG. 19F summarizea comparison of diffusion spectrum imaging (DSI) 212 and DBSI 214 fromhuman subjects 216. DSI 212 is a method that attempts to directlymeasure the probability distribution function of the displacement ofwater molecules without an assumption of tissue structure or the shapeof probability distribution function. It was proposed to identifymultiple fibers within an image voxel (see FIG. 19C). The use oforientation distribution function (ODF) by DSI (see FIG. 19A)effectively estimates angles of crossing fibers (see FIG. 19B). However,its ODF based analysis does not offer other crucial quantitativeinformation of water diffusion relevant to tissue physiology andpathology such as the apparent diffusion coefficients, diffusionanisotropy, or the volume ratio of different components. Therefore,DSI's applications are limited to fiber tracking.

The presence of an isotropic component within the image voxel is animportant biomarker for cell infiltration, edema, and tissue loss. Asshown in FIG. 19F, the isotropic diffusion component is ignored in DSI212 operation for the better estimation of the fiber orientation. Incontrast, DBSI 214 quantitatively separates the isotropic (see FIG. 19D)from fiber component (see FIG. 19E) with accurate isotropic diffusivityassessment.

Operationally, DSI requires high diffusion weighting gradients ofvarious magnitudes and directions to accurately estimate the ODF, atypically impractical challenge on regular clinical MR scanners. Incontrast, DBSI facilitates operation with the clinically used diffusionweighting gradient strength and smaller number of directions. Thus, DBSImay be performed on clinical MR scanners with typical hardwareresources.

FIG. 20A is a diffusion tensor imaging (DTI) 216 for mouse trigeminalnerve embedded in gel and FIG. 20B is a table summarizing theDTI-obtained axial and radial diffusivities. FIG. 21B and FIG. 21C areDBSI anisotropic and isotropic results 218 for mouse trigeminal nerveembedded in gel (see FIG. 21A). DTI 216 derived radial diffusivity isvery dependent on the tissue environment, and inaccurate assessment iscommon due to both the intra- and inter-voxel partial volume effect asdemonstrated in FIG. 20B. Using a simple yet realistic phantomconstructed from fixed mouse trigeminal nerves and gel, as describedabove and as shown in FIG. 21A, DTI 216 significantly over estimated theradial diffusivity 220 (see FIG. 20B), while DBSI 218 correctlyquantified diffusivities 222, anisotropy, and volume ratios of allcomponents (see FIG. 21D).

This phantom study demonstrates the superior results enabled by DBSI inquantifying the overwhelming isotropic component within the image voxeland reporting correct diffusion properties of both the fiber and itsenvironment. Embodiments described herein facilitate correctlyestimating the extent of axonal loss noninvasively (e.g., in a clinicalsetting).

In one embodiment, eight trigeminal nerves from 4 normal male C57BL/6mice were isolated after fixation. Diffusion MR spectroscopy wasperformed at 19° C. using a custom-built surface coil with the followingparameters (common to all nerve fiber measurements): max b=3200 (s/mm²),repetition time (TR) 2 s, echo time (TE) 49 ms, time between applicationof gradient pulses (A) 20 ms, duration of diffusion gradient on time (δ)8 ms, number of averages 4, 99-direction diffusion weighting gradients44. Three diffusion tensor components were observed: anisotropicdiffusion (75.9±2.6%: axon fibers), restricted isotropic diffusion(12.1±0.99%: cells), and non-restricted isotropic diffusion (12.1±2.5%:extra- axonal and extracellular water). The assignment of cell and watercomponents was based on the DBSI-derived spectrum of isotropicdiffusion.

FIG. 24A, FIG. 24B, FIG. 24C, and FIG. 24D are detailed views of theintensities of the scans of FIGS. 23C, 23A, 23D and 23B, respectively.In these figures, FIG. 24A represents intact myelin, FIG. 24B representsintact axons, FIG. 24C represents cell nucleus and FIG. 24D representstissue loss.

Based on DBSI-derived number fibers and the associated fiber principleorientations (Eq. [2]), the detailed composition of each nerve bundlecan be further estimated and classified according to the structureand/or pathology, as illustrated in FIG. 40A (normal axon component2202), FIG. 40B (axon component with myelin damage 2204), FIG. 40C(injured axon component 2206), FIG. 40D (injured and demyelinated axon2208), FIG. 40E (invasive cell component 2210), and FIG. 40F (tissueloss component 2212). Homogenous pathological change in a coherent whitematter tract bundle exhibits a unique signature 4100 of DTI-deriveddirectional diffusivities (FIG. 41A and FIG. 41B). To demonstrate theeffect of complex pathologies, spinal cord white matter, a simple nervebundle without fiber crossing, was examined. To properly model spinalcord white matter lesions containing heterogeneous and co-existingpathologies (FIG. 40B, FIG. 40C, FIG. 40D, FIG. 40E, and FIG. 40F), wemodel diffusion weighted MR signal as a linear combination of a seriesof anisotropic diffusion tensors (representing heterogeneous axon fiberswith different pathology) plus a spectrum of isotropic diffusioncomponents (representing inflammation associated cell infiltration andedema, or tissue loss), Eq. [⁵]:

$\begin{matrix}{S_{k} = {{\sum\limits_{i = 1}^{M}{\sum\limits_{j = 1}^{N}{f_{ij}^{- {{{\overset{\rightarrow}{b}}_{k}{{\cdot \lambda_{\bot{\_ \; i}}}}}}}^{- {{{\overset{\rightarrow}{b}}_{k}{{{\cdot {({\lambda_{\parallel {\_ \; i}} - \lambda_{\bot{\_ \; i}}})}}\cos^{2}\theta_{k}}}}}}}}} + {\sum\limits_{p = 1}^{n}{h_{p}^{{- {{\overset{\rightarrow}{b}}_{k}}} \cdot \lambda_{p}}}}}} & \left( {{Equation}\mspace{14mu} 5} \right)\end{matrix}$

f_(ij) is the non-diffusion weighted signal intensity fraction of theanisotropic tensor delineated by (λ_(⊥) _(_) _(i), λ_(∥) _(_) _(j)). Asdemonstrated by the schematic plot 4100 in FIG. 41B, λ_(⊥) _(_) _(i) arethe i^(th) (i=1,2, . . . , M) radial diffusivity uniformly distributedwithin the limits of [0,2] (μm²/ms); λ_(∥) _(_) _(j) are the j^(th)(i=1,2, . . . , N) axial diffusivity uniformly discretized within thelimits of [1.1, 20]×λ_(⊥) _(_) _(i). M×N is the total number of possibleanisotropic tensor types distributed within physiological andpathological ranges, which can be classified into five groups: normalaxon 2202; demyelinated axon 2204 (increased λ_(⊥) _(_) _(i), andunchanged λ_(∥) _(_) _(j)); injured axon 2206 (unchanged λ_(⊥) _(_)_(i), and decreased λ_(∥) _(_) _(j)); injured axon with demyelination2208 (increased λ_(⊥) _(_) _(i), and decreased λ_(∥) _(_) _(j)), andtissue loss 2212 (significantly increased λ_(∥) _(_) _(j) or λ_(⊥) _(_)_(j)). Mean−2×STD of DBSI-derived λ_(∥) on normal spinal cord whitematter is used as threshold to define the decreased λ_(∥) _(_) _(j);λ_(∥) _(_) _(j)>Mean−6×STD indicates significant λ_(∥) _(_) _(j)increase. Similarly, Mean+2×STD of DBSI-derived λ_(⊥) is used asthreshold to define the increased λ_(⊥) _(_) _(j). h_(p) is thenon-diffusion weighted signal intensity fraction of the p^(th) (p=1, 2,. . . , H) isotropic tensor with mean diffusivity λ_(p) uniformlydistributed within the range of [0,3] (μm²/ms). In the present pilotstudy, a diffusion-weighting scheme with K=100 distinct b-values anddirections uniformly distributed on 3D Cartesian grid was employed. Thedetailed composition of the spinal cord white matter 2200 described byf_(ij) together with the isotropic diffusion spectrum described by h_(p)is determined by solving equation [5] through a regularized nonnegativeleast-squares (NNLS) analysis 4102 (FIG. 41A). The a priori informationof nonnegative signal intensity and smooth signal intensity distributionis incorporated as penalty terms to effectively prevent the NNLS fromover-fitting the measured noisy data while retaining the numericalaccuracy of the solution. Based on the results of the second step, thenon-diffusion weighted signal intensity fraction (f_(ij)) of theanisotropic tensors belonging to each group were summed up to computeindividual pathology component map 4100: the normal axon density 2202;demyelinated axon density 2204; injured axon density; injured anddemyelinated axon density 2208; and density map of tissue loss 2212.Isotropic diffusion component 2210 was computed as the summation offractions from all the isotropic components (h_(p)). The classicimmunohistochemical SMI-31+ staining for the intact axons wasapproximated by the summation of groups 2202 and 2204 (see FIG. 23A);SMI-32+ map (staining for injured axons) by the summation of groups 2206and 2208 (see FIG. 23B); MBP+ map (staining for axons with intactmyelin) by the summation of groups 2202 and 2206 (see FIG. 23C); DAPI+map (staining for cell nucleus) by group 2210 (see FIG. 23D). Examplesare shown in FIGS. 24A, 24B, 24C, 24D, 25A, 25B, 25C, 25D, 25E, 25F,26A, 26B, 26C, 26D, 26E, 26F, and 27A, 27B, 27C, 27D, 27E, and 27F.

FIG. 29 illustrates a DAPI 224 and SMI-31 226 staining of a fixed mousetrigeminal nerve and a comparison of isotropic diffusion spectra withgel. In such an embodiment, nucleus and axon staining was performedusing 4′,6′-diamidino-2-phenylindole (DAPI) and phosphorylatedneurofilament (SMI-31) to count cells (4109±629/mm²) and axons(25434±8505/mm²). The powder-average effect of the 25% (FIG. 30)isotropic diffusion component in the fixed trigeminal nerve is apparentwhen comparing λ∥ and λ_(⊥) derived using DBSI (λ∥=1.07±0.05 μm²/ms;λ_(⊥)=0.12±0.01 μm2/ms) vs. DTI (λ∥=0.77±0.03 μm²/ms; λ_(⊥)=0.17±0.02μm²/ms). Compared to DBSI, DTI underestimated λ∥ by 28%, whileoverestimating λ_(⊥) by 42%. Five fiber-gel samples were examined at 19°C. using DBSI to quantify anisotropic and isotropic diffusion, and T2WMRI to quantify total gel signal intensity.

The DBSI-determined gel water fraction closely matches that determinedusing T2W MRI as shown in FIG. 31-32, suggesting the potential of DBSIto estimate edematous water from more freely diffusing water in regionsof tissue loss. The derived fiber directional diffusivities with andwithout gel are comparable as shown in FIGS. 33 and 34, indicating thatDBSI can correctly assess fiber diffusion properties in the presence ofedema or tissue loss.

FIG. 35A, FIG. 35B, FIG. 35C, FIG. 35D, FIG. 35E, FIG. 35F, FIG. 35G,FIG. 35H, FIG. 35I, are illustrations of six fixed trigeminal nervesgrouped into three pairs of crossing fibers at 32°, 58°, and 91°juxtaposed with 2% agarose gel. DBSI-estimated crossing fiber angles 300(FIG. 35A, FIG. 35B, and FIG. 35C) compare favorably with those derivedusing an orientation distribution function (ODF) by DSI 302 (FIG. 35D,FIG. 35E, and FIG. 35F) and general q-sampling imaging (GQI) 304 (FIG.35G, FIG. 35H, and FIG. 35I). DBSI-quantified mean fiber 300λ∥=1.14±0.06 μm²/ms, λ_(⊥)=0.12±0.02 μm²/ms agreed well with measuredvalues for a single fiber without gel λ∥=1.07±0.05 μm²/ms,λ_(⊥)=0.14±0.02 μm²/ms. For 91°, 58°, 32° phantoms, DBSI-derived gelpercentages were 15%, 14%, and 50%, in close agreement with T2W MRIdetermined 18%, 13%, and 45%. DSI 302 (FIG. 35D, FIG. 35E, and FIG. 35F)and GQI 304 (FIG. 35G, FIG. 35H, and FIG. 35I) failed to resolvecrossing. λ∥ (FIGS. 33), and λ_(⊥) (FIG. 34) derived from trigeminalnerves with and without gel wereas confirmed by Bland-Altman plots.

To further demonstrate the capability of DBSI to resolve multiplecrossing fibers, a 3-fiber crossing phantom (illustrated in FIG. 36A)was built using fixed mouse trigeminal nerves arranged in an approximateequilateral triangle with inner angles 3602/3604/3606=(75°/55°/50°), asis shown in FIG. 36B.

A SNR dependent Monte Carlo simulation and a Cramér-Rao Lower Bound(CRLB) analysis on a model (two crossing fibers with one non-restrictedisotropic component) and diffusion scheme (three-fold tessellatedicosahedric gradient directions, 184 total directions, on two shells:b1/b2=1000, 3500 s/mm2) was performed. FIG. 37A, FIG. 37B, FIG. 37C, andFIG. 37D illustrate the relative CRLB (rCRLB for axial diffusivities(λ_(1∥), λ_(2∥)) of both fibers, and the volume ratio (f2) and radialdiffusivity (λ_(2⊥)) of the second fiber as a function of SNR.

FIGS. 38A and 38B are graphs pertaining to diffusion MRI datarepresentative of a single-fiber with restricted isotropic diffusion andnonrestricted isotropic diffusion were generated in silico via MonteCarlo simulations. The in silico generated data mimicked in vivo mousespinal cord white-matter diffusion properties at the peak of EAE: singlefiber (white-matter tract, λ_(∥)=1.8 μm²/ms, λ_(⊥)=0.24 μm²/ms, along zdirection, fiber fraction 55%), restricted isotropic component(infiltrating cells, ADC=0.17 μm²/ms, cell fraction 26%), andnonrestricted isotropic component (edema, ADC=1.8 μm²/ms, 19%). Allmodel parameters were estimated accurately at SNR=40, typical of our invivo mouse spinal-cord measurements, with bias <15% (FIG. 10). MCsimulation and CRLB derived variances agreed with each other, andimproved with SNR. These results confirm that DBSI-derived diffusionparameters have sufficient precision to permit meaningful estimates offiber ratio, water ratio, cell ratio, cell ADC, and fiber diffusivitiesin mice in vivo. Results suggest that with CRLB optimization at the samemax b-value the precision can be improved by optimizing diffusiondirections (˜40% improvement vs. the prototype DBSI). The optimizeddirections with increased max b-value (=5000) yielded ˜140% improvementover the prototype DBSI (b-value in s/mm²), as is shown in FIG. 39.

A cross-sectional study was performed on 12 B6-EAE mice spinal cords atbaseline (control), onset, peak, and chronic states, followed by IHC(N=5 for each time point). In the representative mouse, λ_(∥) decreasedat the peak (FIG. 42C) and recovered slightly at the chronic EAE stage(FIG. 42D), consistent with decreased SMI-31 staining (FIG. 42O)followed by the recovery of the staining (FIG. 42P) as is shown by FIGS.42M, 42N, 42O, 42P, and 43. Increased λ_(⊥) was seen at EAE peak (FIG.42G) and continued to increase to the chronic EAE stage (FIG. 42H),consistent with the MBP staining gradually losing its intensity asillustrated in FIGS. 42Q, 42R, 42S, 42T, and 44.

DBSI revealed cell infiltration at peak EAE, consistent with DAPIstaining and clearly indicating the presence of inflammation (FIGS. 42U,42V, 42W, 42X, and 45). Quantitative analysis of the ventrolateral whitematter DBSI parameters closely reflects the same pathology profilesuggested by IHC shown in FIGS. 43, 44, 45, and 46. DBSI reflects axonand myelin injury more accurately than that previously determined byDTI, and correctly depicts inflammatory pathological features of thespinal cord white matter from EAE mice in terms of both cellinfiltration and vasogenic edema as shown in FIGS. 45 and 46.

A segment of autopsy cervical spinal cord, fixed in 10% formalin, from54 years old Caucasian female with 22-year disease duration was examinedon a 4.7-T preclinical MR scanner: Varian DirectDrive™ console, 15-cminner diameter, actively shielded Magnex gradient coil (60 G/cm, 270 μsrise time). Tissue contained in a 3-ml syringe with 10% formalin wasplaced in a custom-made solenoid coil for data acquisition using thefollowing parameters: TR 2s, TE 39 ms, A 20 ms, δ 8 ms, slice thickness0.5 mm, number of slices 5, field-of-view 2.4×2.4 cm2, number ofaverages 1, data matrix 192×192.

Diffusion sensitizing gradients were applied in 99 directions with maxb-value=3200 s/mm². In plane resolution was 125×125 μm². DBSI/DTI mapswere coregistered with IHC images and an ROI analysis was employed afterco-registration of MRI and IHC images as shown in FIGS. 47A, 47B, 47C,47D, 48A, 48B, 48C, 48D, 48E, 48F, 48G, 48H, and 48I. Diffusewhite-matter injury was present in the dorsal column, consistent withthe recorded upper extremity numbness of this patient. Significantlyincreased cell infiltration was seen in all three ROIs, consistent withDAPI staining. The effect of infiltrating cells on diffusion is evidentby examining DTI-derived λ_(∥) at (0.36±0.02 μm²/ms) and (0.31±0.01μm²/ms; total 16 image voxels, p=0.07) from the left and right ROI ofthe dorsal column, where more cell infiltration was noted. In contrast,DBSI-derived λ_(∥) at the left (0.81±0.03 μm²/ms) and right (0.74±0.03μm²/ms; total 16 voxels, p=0.0005) ROI was significantly different,revealing more axonal injury at the right ROI, consistent with theSMI-31 staining. Similarly, DBSI-derived λ_(⊥) reveals that the severityof demyelination is again consistent with the MBP staining. Thisco-registered ROI analysis confirms that DBSI is consistent with IHCfindings (FIGS. 47A, 47B, 47C, 47D, 48A, 48B, 48C, 48D, 48E, 48F, 48G,48H, and 48I).

Spherical Harmonic Decomposition (SHD) has been proposed as a method forclassifying imaging voxels into isotropic, single-, and multi-fibercomponents based on SHD coefficients. However, SHD cannot accuratelyestimate the intra-voxel fiber numbers, fiber volume fractions, fiberanisotropy, or fiber orientations. Even in the simple case of twofibers, it is not possible to use SHD to uniquely determine theintra-voxel fiber numbers and orientation since both the volume fractionand relative fiber orientations interfere with the higher order SHDcomponents in a similar fashion. Similar to DSI, SHD also requires highdiffusion weighting gradients. In contrast, DBSI facilitates separatingand quantifying the isotropic and individual anisotropic (fiber)components while maintaining the use of low diffusion weighting gradientmagnitudes.

Q-ball imaging of the human brain is a method closely related to DSI. InDSI, the ODF is reconstructed by sampling the diffusion signal on aCartesian grid, Fourier transformation, followed by the radialprojection. Q-ball imaging acquires the diffusion signal spherically andreconstructs the ODF directly on the sphere. The spherical inversion isaccomplished with the reciprocal space funk radon transform (FRT), atransformation of spherical functions that maps one function of thesphere to another. Q-ball and DSI are theoretically equivalent andgenerate similar ODF. However, q-ball methods are not capable ofestimating fiber angles as well as quantifying multiple tensorparameters.

Independent Component Analysis (ICA) has been proposed for applicationin DTI tractography to recover multiple fibers within a voxel. Althoughthe angle of crossing fibers within voxels can be estimated to within 20degrees of accuracy, eigenvalues cannot be recovered to obtain thecomplete tensor information such as the Fractional Anisotropy (FA).

Moreover, it has been proposed to use a high angular resolutiondiffusion imaging (HARDI) data set as a method that is capable ofdetermining the orientation of intra-voxel multiple fibers. For example,up to 2 fiber components and one isotropic component may be considered.Similar to DBSI, HARDI methods have employed a mixed Gaussian modelincorporating the isotropic diffusion component. However, HARDI is verydifferent in nature compared with DBSI. For example, (i) HARDI fails invoxels with more than 2 fibers; (ii) HARDI does not work in voxels withmore than 1 isotropic component, which is commonly seen in pathologicalconditions with both cell infiltration and edema; (iii) HARDI fails tocompute isotropic diffusivity, improving fiber orientation estimation atthe expense of removing the isotropic diffusion component; (iv) HARDIcannot compute the absolute axial and radial diffusivities for eachcomponent fiber; (v) HARDI cannot compute the true volume fractions ofeach fiber or isotropic component. In contrast, DBSI facilitatesachieving all the goals enumerated above because it may be used to solvefor issues that HARDI ignores or simplifies. HARDI-based methods haveaimed to enhance the tools available for fiber tracking but do notcompute the directional diffusivities of fibers, the isotropicdiffusivity, or true volume fractions.

In summary, diffusion MRI methods in the field currently focus ondetermining the primary orientation of crossing fibers within one voxel.To achieve this goal, most have to relax the condition needed foraccurate estimation of diffusivity or the volume ratio of individualcomponent. DBSI facilitates not only resolving the primary direction ofeach fiber component, but also identifying and quantifying one or moreother physical properties available from the diffusion measurements.

With the quantified fraction, axial diffusivity, and radial diffusivityof each fiber as well as the fraction and mean diffusivity of eachisotropic diffusion tensor, CNS white matter pathology mapscorresponding to the classic immunohistochemistry staining of excisedtissues may be generated. For example, based on the axial diffusivitydistribution intact 2202 (or injured 2204) axonal fiber tract fractionmay be estimated and the fraction distribution map may be generated toreflect the classic phosphorylated neurofilament (SMI-31, for intactaxons, shown illustrated in FIG. 23A), or dephosphorylated neurofilament(SMI-32, for injured axons, shown illustrated in FIG. 23B), staining.The restricted isotropic diffusion component estimated using DBSIconstitutes a map of cell distribution (shown illustrated in FIG. 23D)corresponding to nucleus counting using DAPI staining on the fixedtissue allowing a direct estimate the extent of inflammation in patientCNS white matter.

In this disclosure, we have developed a method incorporating thediffusion profile of each component within the image voxel to performthe tissue classification based on the raw diffusion MRI data. This is anovel approach that has never been demonstrated previously. The typicalclassification is performed using the generated parameters, not thesource data. Our approach generates realistic “noninvasive histology”maps of various CNS white matter pathologies directly related to theactual immunohistochemistry staining that is only available after tissueexcision and fixation. Although an accurate assessment of the underlyingwhite matter pathologies may or may not correctly reflect clinicalsymptoms during the early phase of the disease, it would likely predictthe long-term patient disability. Such a quantitative assessment of CNSwhite matter tracts integrity would enable a patient based interventionclinically. For example, current MS treatments follow a standard dosingregimen, with limited opportunity to adjust management for individualpatient responses. By quantitatively distinguishing and trackinginflammation, and axon and myelin injury, DBSI will provide theopportunity for efficacy assessment of disease-modifying interventionsand allow treatment planning to reflect individual patient response.

Exemplary embodiments of methods, systems, and apparatus for use indiffusion basis spectrum imaging are described above in detail. Themethods, systems, and apparatus are not limited to the specificembodiments described herein but, rather, operations of the methodsand/or components of the systems and/or apparatus may be utilizedindependently and separately from other operations and/or componentsdescribed herein. Further, the described operations and/or componentsmay also be defined in, or used in combination with, other systems,methods, and/or apparatus, and are not limited to practice with only thesystems, methods, and apparatus described herein.

A computer or processor, such as those described herein, includes atleast one processor or processing unit and a system memory. The computeror processor typically has at least some form of computer readablemedia. By way of example and not limitation, computer readable mediainclude computer storage media and communication media. Computer storagemedia include volatile and nonvolatile, removable and non-removablemedia implemented in any method or technology for storage of informationsuch as computer readable instructions, data structures, programmodules, or other data. Communication media typically embody computerreadable instructions, data structures, program modules, or other datain a modulated data signal such as a carrier wave or other transportmechanism and include any information delivery media. Those skilled inthe art are familiar with the modulated data signal, which has one ormore of its characteristics set or changed in such a manner as to encodeinformation in the signal. Combinations of any of the above are alsoincluded within the scope of computer readable media.

Although the present invention is described in connection with anexemplary imaging system environment, embodiments of the invention areoperational with numerous other general purpose or special purposeimaging system environments or configurations. The imaging systemenvironment is not intended to suggest any limitation as to the scope ofuse or functionality of any aspect of the invention. Moreover, theimaging system environment should not be interpreted as having anydependency or requirement relating to any one or combination ofcomponents illustrated in the exemplary operating environment. Examplesof well-known imaging systems, environments, and/or configurations thatmay be suitable for use with aspects of the invention include, but arenot limited to, personal computers, server computers, hand-held orlaptop devices, multiprocessor systems, microprocessor-based systems,set top boxes, programmable consumer electronics, mobile telephones,network PCs, minicomputers, mainframe computers, distributed computingenvironments that include any of the above systems or devices, and thelike.

Embodiments of the invention may be described in the general context ofcomputer-executable instructions, such as program components or modules,executed by one or more computers or other devices. Aspects of theinvention may be implemented with any number and organization ofcomponents or modules. For example, aspects of the invention are notlimited to the specific computer-executable instructions or the specificcomponents or modules illustrated in the figures and described herein.Alternative embodiments of the invention may include differentcomputer-executable instructions or components having more or lessfunctionality than illustrated and described herein. A software moduleor program module may reside in random access memory (RAM), flashmemory, read-only memory (ROM), erasable programmable read-only memory(EPROM), electrically erasable programmable read-only memory (EEPROM),registers, hard disk memory, a removable disk, a CD-ROM, or any otherform of computer-readable storage medium known in the art.

The order of execution or performance of the operations in theembodiments of the invention illustrated and described herein is notessential, unless otherwise specified. That is, the operations may beperformed in any order, unless otherwise specified, and embodiments ofthe invention may include additional or fewer operations than thosedisclosed herein. For example, it is contemplated that executing orperforming a particular operation before, contemporaneously with, orafter another operation is within the scope of aspects of the invention.

It will be understood by those of skill in the art that information andsignals may be represented using any of a variety of differenttechnologies and techniques. For example, data, instructions, commands,information, signals, bits, symbols, and/or chips may be represented byvoltages, currents, electromagnetic waves, magnetic fields or particles,optical fields or particles, or any combination thereof. Similarly, thevarious illustrative logical blocks, modules, circuits, and algorithmoperations described herein may be implemented as electronic hardware,computer software, or a combination of both, depending on theapplication and the functionality. Moreover, the various logical blocks,modules, and circuits described herein may be implemented or performedwith a general purpose computer, a digital signal processor (DSP), anapplication specific integrated circuit (ASIC), a field programmablegate array (FPGA), or other programmable logic device, discrete gate ortransistor logic, discrete hardware components, or any combinationthereof designed to perform the functions described herein. Exemplarygeneral purpose processors include, but are not limited to onlyincluding, microprocessors, conventional processors, controllers,microcontrollers, state machines, or a combination of computing devices.

When introducing elements of aspects of the invention or embodimentsthereof, the articles “a,” “an,” “the,” and “said” are intended to meanthat there are one or more of the elements. The terms “comprising,”including,” and “having” are intended to be inclusive and mean thatthere may be additional elements other than the listed elements.

This written description uses examples to disclose the invention,including the best mode, and also to enable any person skilled in theart to practice the invention, including making and using any devices orsystems and performing any incorporated methods. The patentable scope ofthe invention is defined by the claims, and may include other examplesthat occur to those skilled in the art. Such other examples are intendedto be within the scope of the claims if they have structural elementsthat do not differ from the literal language of the claims, or if theyinclude equivalent structural elements with insubstantial differencesfrom the literal language of the claims.

What is claimed is:
 1. A method of performing diffusion basis spectrumimaging (DBSI) within a tissue of a patient using diffusion magneticresonance data acquired from a portion of the tissue, the diffusionmagnetic resonance data comprising a plurality of diffusion MR signalsassociated with one voxel, the one voxel representing an image of theportion of the tissue, the method comprising: computing, by a processor,an isotropic diffusion portion of the diffusion magnetic resonance datarepresenting isotropic diffusion within the one voxel, the isotropicdiffusion portion comprising a fraction of the diffusion magneticresonance data representing isotropic diffusion; and dividing, by theprocessor, the isotropic diffusion portion into a restricted isotropicdiffusion portion and a non-restricted isotopic diffusion portion, therestricted isotropic diffusion portion comprising a fraction of theisotropic diffusion portion with an apparent diffusion coefficient ofless than 0.3, the non-restricted isotopic diffusion portion comprisinga fraction of the isotropic diffusion portion with an apparent diffusioncoefficient of at least 0.3.
 2. The method of claim 1, wherein computingthe isotropic diffusion portion of the diffusion magnetic resonance datacomprises: projecting, by the processor, the diffusion magneticresonance data onto a diffusion basis set comprising a plurality ofanisotropic diffusion bases and a plurality of isotropic diffusioncomponents, each anisotropic diffusion basis of the plurality ofanisotropic diffusion bases defined by: an axial diffusivity, a radialdiffusivity, an anisotropic basis ratio, and an anisotropic basisdirection; each isotropic diffusion component defined by an isotropiccomponent signal ratio, and an isotropic component diffusivity;iteratively adjusting, by the processor, at least one of: the axialdiffusivity, the radial diffusivity, the anisotropic basis ratiodefining at least one of the plurality of anisotropic diffusion basesand the axial diffusivity, the radial diffusivity, and the anisotropicbasis ratio defining at least one of the plurality of isotropicdiffusion components to minimize a fitting error between the pluralityof diffusion MR signals and a diffusion weighted signal estimated usingthe diffusion basis set; and computing, by the processor, the isotropicdiffusion portion by dividing a sum of all isotropic component signalratios defining the plurality of isotropic diffusion components by atotal voxel signal.
 3. The method of claim 2, wherein dividing theisotropic diffusion portion into the restricted isotropic diffusionportion and the non-restricted isotopic diffusion portion comprises:identifying, by the processor, a first group of isotropic diffusionportions defined by isotropic component diffusivities of less than 0.3;identifying, by the processor a second group of isotropic diffusionportions defined by isotropic component diffusivities of at least 0.3;calculating, by the processor, the restricted isotropic diffusionportion by dividing the isotropic component signal ratios defining thefirst group of isotropic diffusion portions by the total voxel signal;and calculating, by the processor, the non-restricted isotropicdiffusion portion by dividing the isotropic component signal ratiosdefining the second group of isotropic diffusion portions by the totalvoxel signal.
 4. The method of claim 1, wherein the restricted isotropicdiffusion portion within the voxel represents an amount of intracellularfluid within the portion of the tissue.
 5. The method of claim 4,wherein the intracellular fluid is situated within one or moreinflammatory cells, and an elevated value of restricted isotropicdiffusion portion is indicative of an inflammatory condition of theportion of the tissue.
 6. The method of claim 1, wherein thenon-restricted isotropic diffusion portion within the voxel representsan amount of interstitial or extracellular fluid within the portion ofthe tissue.
 7. The method of claim 6, wherein an elevated non-restrictedisotropic diffusion portion is indicative of an edema condition or aloss of cellular structures within the portion of the tissue.
 8. Amethod of diagnosing an inflammatory disorder within a tissue of apatient using diffusion basis spectrum imaging (DBSI) data derived fromdiffusion magnetic resonance data acquired from a portion of the tissue,the diffusion magnetic resonance data comprising a plurality ofdiffusion MR signals associated with one voxel, the one voxelrepresenting an image of the portion of the tissue, the methodcomprising: computing, by a processor, an isotropic diffusion portion ofthe diffusion magnetic resonance data representing isotropic diffusionwithin the one voxel, the isotropic diffusion portion comprising afraction of the diffusion magnetic resonance data representing isotropicdiffusion; and dividing, by the processor, the isotropic diffusionportion into a restricted isotropic diffusion portion and anon-restricted isotopic diffusion portion, the restricted isotropicdiffusion portion comprising a fraction of the isotropic diffusionportion with an apparent diffusion coefficient of less than 0.3, thenon-restricted isotopic diffusion portion comprising a fraction of theisotropic diffusion portion with an apparent diffusion coefficient of atleast 0.3; comparing, by the processor, the restricted isotropic portionto an inflammation threshold; identifying, by the processor, aninflammatory condition within the portion of the tissue if therestricted isotropic portion exceeds the inflammation threshold.
 9. Themethod of claim 8, wherein computing the isotropic diffusion portion ofthe diffusion magnetic resonance data comprises: projecting, by theprocessor, the diffusion magnetic resonance data onto a diffusion basisset comprising a plurality of anisotropic diffusion bases and aplurality of isotropic diffusion components, each anisotropic diffusionbasis of the plurality of anisotropic diffusion bases defined by: anaxial diffusivity, a radial diffusivity, an anisotropic basis ratio, andan anisotropic basis direction; each isotropic diffusion componentdefined by an isotropic component signal ratio, and an isotropiccomponent diffusivity; iteratively adjusting, by the processor, at leastone of: the axial diffusivity, the radial diffusivity, the anisotropicbasis ratio defining at least one of the plurality of anisotropicdiffusion bases and the axial diffusivity, the radial diffusivity, andthe isotropic basis ratio defining at least one of the plurality ofisotropic diffusion components to minimize a fitting error between theplurality of diffusion MR signals and a diffusion weighted signalestimated using the diffusion basis set; and computing, by theprocessor, the isotropic diffusion portion by dividing a sum of allisotropic component signal ratios defining the plurality of isotropicdiffusion components by a total voxel signal.
 10. The method of claim 9,wherein dividing the isotropic diffusion portion into the restrictedisotropic diffusion portion and the non-restricted isotopic diffusionportion comprises: identifying, by the processor, a first group ofisotropic diffusion portions defined by isotropic componentdiffusivities of less than 0.3; identifying, by the processor, a secondgroup of isotropic diffusion portions defined by isotropic componentdiffusivities of at least 0.3; calculating, by the processor, therestricted isotropic diffusion portion by dividing the isotropiccomponent signal ratios defining the first group of isotropic diffusionportions by the total voxel signal; and calculating, by the processor,the non-restricted isotropic diffusion portion by dividing the isotropiccomponent signal ratios defining the second group of isotropic diffusionportions by the total voxel signal.
 11. The method of claim 8, whereinthe restricted isotropic diffusion portion within the voxel representsan amount of intracellular fluid within the portion of the tissue. 12.The method of claim 11, wherein the intracellular fluid is situatedwithin one or more inflammatory cells, and an elevated value ofrestricted isotropic diffusion portion is indicative of an increasednumber of inflammatory cells.
 13. The method of claim 8, wherein thenon-restricted isotropic diffusion portion within the voxel representsan amount of interstitial or extracellular fluid within the portion ofthe tissue.
 14. The method of claim 13, wherein an elevatednon-restricted isotropic diffusion portion is indicative of an edemacondition or a loss of cellular structures within the portion of thetissue.